Thermography for disease detection in livestock: A scoping review

Rosie McManus, Lisa Boden, William Weir, Lorenzo Viora, Robert Barker, Yunhyong Kim, Pauline McBride, Shutan Yang

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. In this review, we investigate parameters that are essential to the progression of this tool and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. This review demonstrates the inconsistencies in practice across peer-reviewed articles and reveals that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species. We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection in livestock.

Original languageEnglish
Pages (from-to)1-28
Number of pages28
JournalFrontiers in Veterinary Science
Early online date9 Aug 2022
DOIs
Publication statusPublished - 9 Aug 2022

Keywords / Materials (for Non-textual outputs)

  • surveillance
  • veterinary
  • disease
  • thermography
  • livestock
  • infra-red

Fingerprint

Dive into the research topics of 'Thermography for disease detection in livestock: A scoping review'. Together they form a unique fingerprint.

Cite this